S. Jayasinghe

Uva Wellassa University

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Lalit Kumar

Shri Vishwakarma Skill University

Farrukh Chishtie

University of British Columbia

Tharani Gopalakrishnan

NSW Department of Planning and Environment

Senaka Basnayake

Asian Disaster Preparedness Center

N. N. Das

Purdue University West Lafayette

Kel Markert

Google (United States)

Eric Anderson

Marshall Space Flight Center

Amor V.M. Ines

Texas A&M University

Peeranan Towashiraporn

Asian Disaster Preparedness Center

Konstantinos M. Andreadis

University of Massachusetts Amherst

Cooperative Institutions

University of New England

Asian Disaster Preparedness Center

University of Minho

Jet Propulsion Laboratory

Universidade do Porto

Universidade de Vigo

Université Clermont Auvergne

National Botanical Research Institute

Council of Scientific and Industrial Research

Agriculture and Food

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Elsevier eBooks (2022)

Dominic Agyei C. Anandharamakrishnan Federico Antonioli Jeetendra Prakash Aryal Thierry Astruc

10.1016/b978-0-323-91001-9.09990-4

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Assessment of Potential Land Suitability for Tea (Camellia sinensis (L.) O. Kuntze) in Sri Lanka Using a GIS-Based Multi-Criteria Approach

Agriculture (2019)

S. Jayasinghe Lalit Kumar Janaki Sandamali

The potential land suitability assessment for tea is a crucial step in determining the environmental limits of sustainable tea production. The aim of this study was to assess land suitability to determine suitable agricultural land for tea crops in Sri Lanka. Climatic, topographical and soil factors assumed to influence land use were assembled and the weights of their respective contributions to land suitability for tea were assessed using the Analytical Hierarchical Process (AHP) and the Decision-Making Trail and Evaluation Laboratory (DEMATEL) model. Subsequently, all the factors were integrated to generate the potential land suitability map. The results showed that the largest part of the land in Sri Lanka was occupied by low suitability class (42.1%) and 28.5% registered an unsuitable land cover. Furthermore, 12.4% was moderately suitable, 13.9% was highly suitable and 2.5% was very highly suitable for tea cultivation. The highest proportion of “very highly suitable” areas were recorded in the Nuwara Eliya District, which accounted for 29.50% of the highest category. The model validation results showed that 92.46% of the combined “highly suitable” and “very highly suitable” modelled classes are actual current tea-growing areas, showing the overall robustness of this model and the weightings applied. This result is significant in that it provides effective approaches to enhance land-use efficiency and better management of tea production.

Sri lanka

Suitability analysis

Land Cover

10.3390/agriculture9070148

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Citations (57)

Deriving Daily Discharges from Satellite Radar Altimetry and Ensemble Learning Regression in Poorly Gauged River Basins

AGU Fall Meeting Abstracts (2018)

D. Kim H. Lee Hanwen Yu S. Jayasinghe Senaka Basnayake

Satellite Altimetry

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Multiscale Landslide Susceptibility Mapping in Myanmar, Southeast Asia

AGU Fall Meeting Abstracts (2020)

SJ Edwards Eric Anderson S. Jayasinghe Anita Dewi

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Growth and yield estimation of banana through mathematical modelling: a systematic review

The Journal of Agricultural Science (2022)

S. Jayasinghe C. J. K. Ranawana I. C. Liyanage P.E. Kaliyadasa

Abstract Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model ‘SIMBA’ and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models.

Predictive modelling

10.1017/s0021859622000259

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Modeling the climate suitability of tea [Camellia sinensis(L.) O. Kuntze] in Sri Lanka in response to current and future climate change scenarios

Agricultural and Forest Meteorology (2019)

S. Jayasinghe Lalit Kumar

Representative Concentration Pathways

10.1016/j.agrformet.2019.03.025

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Citations (109)

Climate change and future of agri-food production